USC Spatial Sciences Institute Announces Master of Science in Spatial Economics and Data Analysis

March 27, 2019 -- John P. Wilson, Professor and Founding Director of the Spatial Sciences Institute (SSI) at the USC Dornsife College of Letters Arts and Sciences, has announced the launch of its  Master of Science   in Spatial  Economics  and Data  Analysis,a two-year graduate degree offered in conjunction with USC’s internationally renowned Dornsife Department of Economics. This program is the second inter-disciplinary M.S. program for SSI; the first  is the   Master of Science  in Spatial  Data Science a two-year graduate degree offered in conjunction with the Viterbi School of Engineering Department of Computer Science, which is set to graduate its third class in the spring of 2019.

“Were excited to offer these innovative data science programs to aspiring data scientists,” said Wilson. “With an increased need for data analytics education in the professional workplace, these two programs were specifically designed with job-training and workforce preparation in mind.” 

Applicants for each program must have completed or be on track to complete their undergraduate degree by Spring of 2019.. In addition: 

  • For the Spatial Economics and Data Analysis program, a background in at least basic undergraduate economics is expected 
  • For the Spatial Data Science  program, programming experience or at least a year of calculus is required. 

 
M.S. in  Spatial  Economics  and Data Analysis 

“This is a specialty degree that we’re truly honored to offer here at SSI, in conjunction with the Department of Economics,” said Wilson. “In fact, we think it’s one of the best spatial data science programs in the country for scientific-minded individuals who are interested in applying economic theory and our modern geospatial infrastructures to creating economic opportunities and solving urban problems. Coursework combines that of a masters in economics with our core data science curriculum.” 

The joint-degree program allows students to work with globally recognized faculty from the Dornsifeâ�¯ Department of Economicsâ�¯and the SSI. Student coursework will help identify business opportunities and address public policy challenges using location-based Big Data with insights from geographic information science (GIS), urban economics, and spatial econometrics.

M.S students will: 

  • Develop an in-depth understanding of the fundamentals of spatial econometrics; 
  • Learn and apply spatial analysis and spatial modeling approaches to identify new business opportunities and new policy solutions addressing urban problems; 
  • Gain valuable research experience in analyzing spatial “Big Data” 
  • Develop professional development insights in this nascent field of spatial analysis. 

A total of 32 units is required for the M.S. in Spatial Economics and Data Analysis. A minimum cumulative GPA of 3.00 is required for graduation. 
 
Applications are due in June. 

Learn Morehttps://spatial.usc.edu/m-s-in-spatial-economics-and-data-analysis/


M.S.  In Spatial  Data Science : 

“The world is awash with data,” Says Susan H. KameiManaging Director of the SSI. “This program is designed to help our students become masters in analytics and learn how to pull actionable data out of massive collections of georeferenced information. We will teach students how to synthesize data in order to assist with real world situations.” 

This masters in spatial data science program cross-disciplinary joint degree program is offered by the Viterbi School of Engineering Department of Computer Science and the Dornsife College of Letters, Arts and Sciences Spatial Sciences Institute. 

The M.S. in Spatial Data Science provides students with the knowledge and skills to: 

  • Understand and contribute toward the significant technical and societal challenges created by large location-based data environments, including architecture, integrity, management, scalability; 
  • Understand how spatial data can be acquired and used to support various forms of analysis, modeling and geo-visualization in large data environments; and 
  • Understand how artificial intelligence and machine learning can be used to augment the typical geographic information science (GIS) concepts and workflows to intelligently  a c quire   data to provide enterprise-centric solutions for a variety of societal challenges and issues spanning the public, private and not-for-profit sectors.  

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